[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116866-en":3,"doc-seo-116866-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116866,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","The Flawed Foundations of Fair Machine Learning","公平机器学习的公平性定义与落地机制长期受到研究关注，但当前范式的根基存在谬误推理、误导性断言与可疑做法。问题源于未能理解：在统计精度与让不同群体获得相似结果之间的权衡，并非主观叙事，而是一种独立外部约束。文中指出文献中实际上只有一种核心公平概念；并证明在存在群体差异的数据情境下，两类目标不可避免地产生权衡，进而对公平实践构成生存性威胁。同时提出概念验证式评估框架，帮助研究者与设计者理解二者关系，并面向数据科学、法学与数据伦理给出后续研究建议。","arXiv :2306 .0 14 17v 1 [ cs .CY] 2 Jun 2023  \nThe Flawed Foundations of Fair Machine Learning  \nRobert Lee Poe 1,2* and Soumia Zohra El Mestari2,3  \n1* LIDER Laboratory, Sant’Anna School of Advanced Studies, Via Santa  \nCecilia 3, Pisa, 56127, Italy.  \n2 Interdisciplinary Center for Security, Reliability and Trust, University of Luxembourg, 6 avenue de la Fonte, Esch-sur-Alzette, L-4364,  \nLuxembourg.  \n*Corresponding author(s). E-mail(s): [roberlee.poe@santannapisa.it](roberlee.poe@santannapisa.it) ;  \nContributing authors: [soumia.elmestari@uni.lu](soumia.elmestari@uni.lu) ;  \nAbstract  \nThe definition and implementation of fairness in automated decisions has been extensively studied by the research community. Yet, there hides fallacious reasoning, misleading assertions, and questionable practices at the foundations of the current fair machine learning paradigm. Those flaws are the result of a failure to understand that the trade-off between statistically accurate outcomes and group similar outcomes exists as independent, external constraint rather than as a subjective manifestation as has been commonly argued. First, we explain that thereis only one conception of fairness present in the fair machine learning literature:  \ngroup similarity of outcomes based on a sensitive attribute where the similarity benefits an underprivileged group. Second, we show that there is, in fact, a trade-off between statistically accurate outcomes and group similar outcomes in any data setting where group disparities exist, and that the trade-off presentsan existential threat to the equitable, fair machine learning approach. Third, we introduce a proof-of-concept evaluation to aid researchers and designers in understanding the relationship between statistically accurate outcomes and group similar outcomes. Finally, suggestions for future work aimed at data scientists, legal scholars, and data ethicists that utilize the conceptual and experimental framework described throughout this article are provided. ∗  \nKeywords: Accuracy, Equity, Merit, Automated Decisions, Fair Machine Learning, Algorithmic Fairness, Algorithmic Discrimination  \n∗ This article is a preprint submitted to the Minds and Machines Special Issue on the (Un)fairness of AI on May 31st, 2023 .  \n1  \n1 Introduction  \nAutomated decision-making systems are increasingly being used to render high-impact decisions regarding human beings. All the while, notorious accounts of algorithmic discrimination and algorithmic unfairness have been reported by news outlets over the past decade. Due to the many concerns about the potential societal impacts of machine learning, governments are beginning to put forward policy positions and draft regulations. In the AI Bill of Rights, the White House states that automated decisions should be designed and deployed to achieve equitable outcomes. In Europe, the AI Act states that automated decisions should not perpetuate historic patterns of discrimination or create new forms of disparate impact. Right now, policy-makers and regulators are relying heavily on the fair machine learning community to present solutions. However, a unipolar conception of fairness is being represented and advocated for by the fair machine learning community, which does not reflect the same breadth of opinion that exists in wider society.1  \nTo a limited extent, researchers in the field have understood that they had not happened upon an empty field (of research) but instead a garden that has been fostered, cared for, and in some cases ignored for a very long time. Perspectives from many domains have been incorporated into the literature: legal doctrines like disparate impact (Barocas and Selbst, 2016), fair distribution philosophies dealing with egalitarianism and merit (Arif Khan et al, 2022), socio-technical critiques of technological solutionism (Cooper et al, 2021; Selbst et al, 2019), and concepts from feminist communications and data science like the myth of objectivity and","cbCaicUt7o0Hjdd3","https://ap.wps.com/l/cbCaicUt7o0Hjdd3","pdf",988212,1,20,"English","en",105,"# Abstract\n# 1 Introduction\n## Fairness in policy and regulation\n## Multidisciplinary perspectives and terminology\n# 2 Fair Machine Learning Means Group Similarity in Outcomes","[{\"question\":\"文中认为公平机器学习范式的主要问题是什么？\",\"answer\":\"文中指出问题在于基础层面的谬误推理、误导性断言与可疑实践，根源是对“统计准确性与群体相似性之间权衡”的性质理解偏差。\"},{\"question\":\"文献中关于公平的核心概念到底有几种？\",\"answer\":\"作者认为公平机器学习文献中实际只有一种公平观：当相似性对弱势群体有利时，群体之间的结果相似。\"},{\"question\":\"统计准确性与群体相似性之间的权衡意味着什么？\",\"answer\":\"文中说明，只要数据情境中存在群体差异，这种权衡在任何设置下都存在，并对公平机器学习方法构成迫在眉睫的威胁。\"},{\"question\":\"文中如何帮助研究者理解二者关系？\",\"answer\":\"文中提出概念验证式（proof-of-concept）的评估，用于理解统计准确性与群体相似性之间的关系，并在此基础上给出后续研究建议。\"}]","The Flawed Foundations of Fair Machine Learning | PDF",1785672141,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"the-flawed-foundations-of-fair-machine-learning","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-flawed-foundations-of-fair-machine-learning/116866/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"文中认为公平机器学习范式的主要问题是什么？","Question",{"text":75,"@type":76},"文中指出问题在于基础层面的谬误推理、误导性断言与可疑实践，根源是对“统计准确性与群体相似性之间权衡”的性质理解偏差。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"文献中关于公平的核心概念到底有几种？",{"text":80,"@type":76},"作者认为公平机器学习文献中实际只有一种公平观：当相似性对弱势群体有利时，群体之间的结果相似。",{"name":82,"@type":73,"acceptedAnswer":83},"统计准确性与群体相似性之间的权衡意味着什么？",{"text":84,"@type":76},"文中说明，只要数据情境中存在群体差异，这种权衡在任何设置下都存在，并对公平机器学习方法构成迫在眉睫的威胁。",{"name":86,"@type":73,"acceptedAnswer":87},"文中如何帮助研究者理解二者关系？",{"text":88,"@type":76},"文中提出概念验证式（proof-of-concept）的评估，用于理解统计准确性与群体相似性之间的关系，并在此基础上给出后续研究建议。","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,118,123,126,130,133,137],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":29,"slug":117},6,"Technology","technology",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":124,"slug":125},30,"research-report",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":21,"slug":129},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":21,"slug":132},"World Cup","world-cup",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":134,"slug":136},10,"Lifestyle","lifestyle",{"id":138,"doc_module":4,"doc_module_name":46,"category_name":139,"show_sort_weight":110,"slug":140},19,"General","general"]